A Simple, Sustainable, Integrative Analytical and Predictive Approach for Actualizing Precision Medicine for Cancer Management: A Model for Resource-Limited Settings

Abstract

In spite of our knowledge of the strong influence genomic variations have on how the human body metabolize drugs, health, diseases across diverse populations, majority of the testing and trying of the current chemotherapeutic drugs used in Kenya (and Africa), if not all is done in developed countries. Of essence, despite efforts to include diverse backgrounds, majority of the populations used in these clinical trials have potentially different genetic make-up, therefore not true representatives of African populations. As a result, use of these chemotherapeutic drugs is characterized by high failure rates, relapses and low survival rates. Consequently, new African population-specific therapeutic targets, diagnostic and prognostic biomarkers for effectively tailor-making clinical decisions regarding selection and dosage of these drugs are urgently required. In addition to improve effectiveness and provide a more targeted approach with the view thus minimizing toxicity, adverse effects and optimizing the drugs’ safety. In line with that, several targeted therapies have shown great promise in cancer management in comparison to the non-selective cytotoxic drug therapies. To leverage on this promise, Precision Medicine approach whereby individual variations in genomic, anatomical, physiological, environmental and biological factors exposure, human microbiome and lifestyles are taken into account when making clinical care decisions. Notwithstanding the low genomic literacy, testing capacities in resource-limited settings such as Kenya, simple, sustainable integrative analytical, predictive modeling approaches to identify new, independent African populationspecific therapeutic targets, relevant, affordable, readily available genetic testing for the initial diagnosis, prognostic biomarkers for monitoring response to therapy can be adopted to effectively actualize a sustainable Precision Medicine strategy. Such methods or models are currently lacking, while studies with African populations in this hugely potential field of “Big Data” analytics are scarce. By use of Meta-analysis approach of pooling together of treatment effects of five targeted therapeutic strategies, five conventional cytotoxic drug therapies, Radiotherapy in Breast and Colorectal cancers, this study proposes to develop such methods and model applicable to resource-limited settings. The pooled treatment effects of the three therapeutic strategies in the two cancers will be correlated for any significant differences. Subsequently, the pooled treatment effects as the dependent variables, the various individual variabilities as the independent variables and using simple correlations and Multiple Regression Analysis (MRA), least square method, creating dummy variables to build predictive models. Results from this pilot study will inform future larger studies with African populations and guide identification of African population-specific targets, biomarkers and actualization of an effective and sustainable Precision Medicine strategy for cancer management in Kenya and Africa. This will have a direct impact on cancer care, help in attainment of the United Nations Sustainable Development Goal (SDG) number three and Government of Kenya (GOK) “Big Four” agenda of Universal Healthcare Coverage (UHC).